{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "import numpy as np\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "temp = np.random.randint(1,100,size=(10,5))\r\n",
    "temp"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[47,  9,  1, 37, 45],\n",
       "       [35, 58, 20, 63, 90],\n",
       "       [ 9, 78, 28, 86, 54],\n",
       "       [55, 93, 38, 26, 48],\n",
       "       [17, 29, 99, 26, 77],\n",
       "       [71, 56, 93, 80, 68],\n",
       "       [28, 84, 61, 19, 39],\n",
       "       [ 3, 75, 85, 82, 83],\n",
       "       [81, 95, 88, 47, 16],\n",
       "       [58, 72, 21, 67, 12]])"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "from sklearn.model_selection import train_test_split\r\n",
    "x_train, x_test = train_test_split(temp, test_size=0.4, shuffle=True)\r\n",
    "x_test"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[ 9, 78, 28, 86, 54],\n",
       "       [58, 72, 21, 67, 12],\n",
       "       [71, 56, 93, 80, 68],\n",
       "       [17, 29, 99, 26, 77]])"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "from sklearn.preprocessing import StandardScaler\r\n",
    "std = StandardScaler()\r\n",
    "x_test = std.fit_transform(x_test)\r\n",
    "x_train = std.transform(x_train)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "# sigmoid函数\r\n",
    "import numpy as np \r\n",
    "import matplotlib.pyplot as plt \r\n",
    "x = np.arange(-5,5,0.01)\r\n",
    "# y = 1/(1 + np.exp(-x))\r\n",
    "y = x*x\r\n",
    "plt.plot(x,y)\r\n",
    "plt.xlabel('x')\r\n",
    "plt.ylabel('y')\r\n",
    "plt.grid()\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.8.6",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.6 32-bit"
  },
  "interpreter": {
   "hash": "df642eef7b43fe7a4a517bca7a97690968a466268416ac555ac71584a4a4c66c"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}